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Inverse Distance Aggregation for Federated Learning With Non-IID Data

MCML Authors

Yousef Yeganeh

→ Group Nassir Navab
Computer Aided Medical Procedures & Augmented Reality

Azade Farshad

Dr.

→ Group Nassir Navab
Computer Aided Medical Procedures & Augmented Reality

Nassir Navab

Prof. Dr.

Principal Investigator

Computer Aided Medical Procedures & Augmented Reality

Abstract

Federated learning (FL) has been a promising approach in the field of medical imaging in recent years. A critical problem in FL, specifically in medical scenarios is to have a more accurate shared model which is robust to noisy and out-of distribution clients. In this work, we tackle the problem of statistical heterogeneity in data for FL which is highly plausible in medical data where for example the data comes from different sites with different scanner settings. We propose IDA (Inverse Distance Aggregation), a novel adaptive weighting approach for clients based on meta-information which handles unbalanced and non-iid data. We extensively analyze and evaluate our method against the well-known FL approach, Federated Averaging as a baseline.

inproceedings

DART DCL @MICCAI 2020

Workshop on Domain Adaptation and Representation Transfer, and Distributed and Collaborative Learning at the 23rd International Conference on Medical Image Computing and Computer Assisted Intervention. Virtual, Oct 04-08, 2020.